| Issue |
E3S Web of Conf.
Volume 415, 2023
8th International Conference on Debris Flow Hazard Mitigation (DFHM8)
|
|
|---|---|---|
| Article Number | 05030 | |
| Number of page(s) | 4 | |
| Section | Case Studies and Hazard Assessments | |
| DOI | https://doi.org/10.1051/e3sconf/202341505030 | |
| Published online | 18 August 2023 | |
Probabilistic Prediction Method of Erosion Volume and Deposition Area from Rainfall Observation Data
1 Kyoto University, Disaster Prevention Research Institute, 8128235, Yoko-oji Shimomisu Higashinokuchi, Fushimi, Kyoto, Japan
2 Kyoto University, Graduate School of Engineering, 8128235, Yoko-oji Shimomisu Higashinokuchi, Fushimi, Kyoto, Japan
3 RIKEN Center for Computational Science, 6500047, Minatojima-Minamimachi, Chuo-ku, Kobe, Japan
* Corresponding author: yamanoi.kazuki.6s@kyoto-u.ac.jp
We propose a methodology to estimate the spatial distribution of the probability of sediment deposition due to debris flow from rainfall data by combining the probability prediction of erosion volume based on an ordinal logistic regression and a sediment transport simulation. By using the Receiver Operating Characteristic (ROC) curve and Area Under Curve (AUC) we have selected the best combination of shortand long-term rainfall indices used as explanatory variables in the ordinal logistic model. The results showed that the regression model using 60-minute and 48-hour rainfall indices performed well and that the regression model using three events improved the predictability of local disasters in 2014. Furthermore, we performed Monte Carlo debris-flow simulations using rainfall data from 2014 using the model. We confirmed that the spatial distribution of disaster probability is consistent with the actual damage.
© The Authors, published by EDP Sciences, 2023
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